PROGAM M.TECH. in Embedded Systems · 18ES701 Embedded Systems for Automotive Applications 3 0 0 3...

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PROGAM

M.TECH. in

Embedded Systems

DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

CURRICULUM AND SYLLABUS

REGULATIONS

Table of Contents

Program Specific Outcomes (PSOs)..........................................................................4

Curriculum .................................................................................................................5

Evaluation Pattern ......................................................................................................8

Syllabi ......................................................................................................................10

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Vision of the Institute

To be a global leader in the delivery of engineering education, transforming individuals to become

creative, innovative, and socially responsible contributors in their professions.

Mission of the Institute:

* To provide best-in-class infrastructure and resources to achieve excellence in technical

education

* To promote knowledge development in thematic research areas that have a positive impact on

society, both nationally and globally,

* To design and maintain the highest quality education through active engagement with all

stakeholders –students, faculty, industry, alumni and reputed academic institutions,

* To contribute to the quality enhancement of the local and global education ecosystem,

* To promote a culture of collaboration that allows creativity, innovation, and entrepreneurship to

flourish, and

* To practice and promote high standards of professional ethics, transparency, and

accountability.

Vision of the Department

Mould generations of electrical and electronics engineers on global standards with multi disciplinary

perspective to meet evolving societal needs.

Mission of the Department

Empower students with knowledge in electrical, electronics and allied engineering facilitated in

innovative class rooms and state-of-the art laboratories.

Inculcate technical competence and promote research through industry interactions, field exposures

and global collaborations.

Promote professional ethics and selfless service

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4

Program Specific Outcomes (PSOs)

PSO1: Acquire state-of-the-art technologies for development of embedded solutions

PSO2: Ability to work in a multidisciplinary environment employing ethical values andsocial responsibility

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M.TECH. EMBEDDED SYSTEMS


CURRICULUM

6

First Semester

CourseCode Type Course L T P Cr

18MA604 FC Applied Mathematics for Embedded Systems 31 0 4

18ES601 FC Embedded System Programming 3 0 2 4

18ES602 FC Signal and Image Processing 3 0 2 4

18ES603 FC Embedded Processor Architectures and Design 3 0 2 4

18RM600 SC Research Methodology 2 0 0 2

E Live in Labs * / Elective – I 3

18HU601 HU Amrita Values Program** P/F

18HU602 HU Career Competency I** P/F

Credits 21** Non-credit course

Second Semester

CourseCode Type Course L T P Cr

18ES621 SC Distributed Computing 3 0 0 318ES622 SC Internet of Things 3 0 2 418ES623 SC Real Time Operating Systems 3 0 2 418ES624 SC FPGA-Based System Design 3 0 2 4

E Elective – II 3

E Elective – III 318ES625 SC Embedded Systems Applications Lab 1 0 2 218HU603 HU Career Competency II 0 0 2 1

Credits 24

Third Semester

CourseCode Type Course L T P Cr18ES798 P Dissertation 8

Credits 8

Fourth Semester

CourseCode Type Course L T P Cr18ES799 P Dissertation 12

Credits1

Total Credits:65

7

List of Courses

Foundation Core

CourseCode Course L T P Cr

18MA604 Applied Mathematics for Embedded Systems 3 1 0 418ES601 Embedded System Programming 3 0 2 418ES602 Signal and Image Processing 3 0 2 418ES603 Embedded Processor Architectures and Design 3 0 2 4

Subject Core

CourseCode Course L T P Cr

18RM600 Research Methodology 2 0 0 218ES621 Distributed Computing 3 0 0 318ES622 Internet of Things 3 0 2 418ES623 Real Time Operating Systems 3 0 2 418ES624 FPGABased System Design 3 0 2 418ES625 Embedded Systems Application Lab 1 0 2 2

ElectivesGroups of Streams

I. Embedded ApplicationsCourseCode Course L T P Cr

18ES701 Embedded Systems for Automotive Applications 3 0 0 318ES702 Advanced Mobile and Wireless Networks 3 0 0 318ES703 Embedded Systems in Biomedical Applications 3 0 0 318ES704 Embedded Systems in Robotics 3 0 0 318ES705 Embedded Systems in Smart Grid 3 0 0 318ES706 Design of Internet of Things and Cloud Computing 2 0 2 318ES707 Special Topics in Embedded Systems 3 0 0 3

II. Architecture and Programmin18ES708 Multi-Core Architectures 3 0 0 318ES709 Fault Tolerant Systems 3 0 0 318ES710 GPU Architecture and Programming 2 0 2 318ES711 Soft Computing 3 0 0 318ES712 Hardware Software Co-Design 3 0 0 318ES713 Object Oriented Programming 3 0 0 318ES714 Machine Learning 3 0 0 318ES715 Deep Learning 2 0 2 3

III. Controls and Systems

18ES716 Cryptography and Network Security 3 0 0 318ES717 Speech and Language Processing 3 0 0 3

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18ES718 Advanced Digital Signal Processing and Processors 3 0 0 318ES719 Modern Control Systems 3 0 0 318ES720 Video Processing 3 0 0 3

Project Work

CourseCode

Course L T P Cr

18ES798 Dissertation 8

18ES799 Dissertation 12

Evaluation Pattern

50:50 (Internal: External) (All Theory Courses)

Assessment Internal External

Periodical 1 (P1) 15

Periodical 2 (P2) 15*Continuous Assessment (CA) 20

End Semester 50

80:20 (Internal: External) (Lab courses and Lab based Courses having 1 Theory hour)


*Continuous Assessment (CA) 80

End Semester 20

70:30(Internal: External) (Lab based courses having 2 Theory hours/ Theory and Tutorial)Theory- 60 Marks; Lab- 40 Marks


Periodical 1 10

Periodical 2 10

*Continuous Assessment(Theory) (CAT)

10

Continuous Assessment (Lab)(CAL)

40

End Semester 30

Grades O to P indicate successful completion

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65:35 (Internal: External) (Lab based courses having 3 Theory hours/ Theory and Tutorial)Theory- 70 Marks; Lab- 30 Marks


Periodical 1 10

Periodical 2 10

*Continuous Assessment(Theory) (CAT)

15

Continuous Assessment (Lab)(CAL)

30

End Semester 35

*CA – Can be Quizzes, Assignment, Projects, and Reports.

LetterGrade

Grade Point Grade Description

O 10.00 Outstanding

A+ 9.50 Excellent

A 9.00 Very Good

B+ 8.00 Good

B 7.00 Above Average

C 6.00 Average

P 5.00 Pass

F 0.00 Fail

of the course

( C xGr )i iCGPA =

C i

Where

Ci = Credit for the ith course in any semester

Gri= Grade point for the ith course

Cr. = Credits for the Course

Gr. = Grade Obtained

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M.TECH. EMBEDDED SYSTEMS


SYLLABI

18MA604 APPLIED MATHEMATICS FOR EMBEDDED SYSTEMS 3-1-0-4

Linear Algebra: Review of Matrices. Vector spaces and subspaces, linear independence, basis anddimensions, linear transformations, orthogonality, projections and least square applications.Eigenvaluesand eigenvectors, Positive Definite Matrices - Minima, Maxima and saddle points, semidefinite andindefinite matrices, Singular value decomposition.

Optimization: Least-squares and linear programming, convex and non-linear optimization. Convex sets,Convex optimization Problems, Optimization problem in standard form, Quasi-convex optimization,linear optimization, quadratic optimization, inequality constraints, Semi definite programming, vectoroptimization. Duality, approximation and fitting, statistical estimation, geometric problems,Unconstrained minimization, gradient descent method, steepest descent method, Newton’s method,Equality constrained minimization, eliminating equality constraints, Newton’s method with equalityconstraints, Interior point method.

Course Outcomes:

CO1 Analysing solvability ofthe linear system of equationsand applying matrix algebra in solving asystem of linear Equations

CO2 Understanding concepts of vector space and the link between linear transformation and matrix.CO3 Understanding the concepts of Gram-Schmidt orthogonalization, Least squares and Singular

value decompositionCO4 Understanding different types of Optimization Techniques in engineering problems. Learning

Optimization methods such as Bracketing methods, Region eliminationmethods,Pointestimationmethods

CO4 Understanding gradient based Optimizations Techniques in single variables as well as multi-variables(non-linear).

TEXT BOOKS / REFERENCES:

1. Gilbert Strang, “Linear Algebra and Its Applications”, Fourth Edition, Cengage, 2006.2. Howard Anton and Chris Rorres ‘Elementary Linear Algebra’, , John Wiley & Sons, 1994, Seventh

Edition.3. Edwin K.P. Chong, Stanislaw H. Zak, “An introduction to Optimization”, 2nd edition, Wiley, 2013.4. Kalyanmoy Deb, “Optimization for Engineering Design: Algorithms and Examples”, Prentice Hall,

2002.5. Stephen P. Boyd and LievenVandenberghe D, “Convex Optimization”, Cambridge University Press,

2004.

18ES601 EMBEDDED SYSTEM PROGRAMMING 3-0-2-4

GNU Tools, Development and debugging Tools. Review of general C programming and data types,arrays, functions, pointers, structure, enum, files. Introduction to Embedded C, Interfacing C withAssembly. Embedded programming issues - Reentrancy, Portability, Optimizing and testing embedded Cprograms. Embedded Applications using Data structures, Linear data structures– Stacks and Queues,

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Linked List. Object Oriented programming introduction and basics, Scripting Languages for EmbeddedSystems - Shell scripting, Programming basics of Python. Software architecture of Embedded System.

Course Outcomes:

CO1 Understanding the basics of C programming, compilation process and tools used.

CO2 Illustrate the usage of functions, pointers, file handling etc.

CO3 Develop data structures for embedded computing

CO4 Discuss embedded system programming issues.Understand OOPs concept, various scripting languages and software architecture for embedded

CO5 systems.

CO6 Develop of application using embedded system programming concepts.


1. Behrouz A. Forouzan and Richard F. Gilberg, “Computer Science: Structured ProgrammingApproach Using C”, Third Edition, Course Technology Inc., 2006.

2. Kirk Zurellm, “C Programming for Embedded Systems”, CRC Press, 2000.3. David E Simon, “An Embedded Software Primer”, Pearson Education Asia, 2005.4. Simon Monk , “Programming the Raspberry Pi: Getting Started with Python”, The McGraw-Hill

Companies, 2013.5. Michael Dawson, “Python Programming for the Absolute Beginner”, Third Edition, Cengage

Learning, 2010.

18ES602 SIGNAL AND IMAGE PROCESSING 3-0-2-4

Signal Processing: Review of Frequency and time domain analysis -Discrete Fourier Transforms, FastFourier Transform. Digital Filters-IIR Filters–Bilinear transformation. FIR filters–Windowing method-Application to real time signals : simulated, audio with noise filtering and analysis of signals.

Image Processing: Elements of Visual Perception- Image Sensing and Acquisition-Simple ImageFormation- Image Sampling and Quantisation—Image Quality-Introduction to colour image-,Introduction to color image – RGB and HSI Models.Image transform: Fourier transform, DFT,Hadamard Transform. Filters- Image enhancement in Spatial domain: Introduction to imageenhancement, basic grey level transforms, Histogram, Histogram-processing equalization, Matching &color histogram, Enhancement using arithmetic/logic operations, Spatial filtering, Smoothing spatialfiltering, Sharpening spatial filtering.Image Enhancement in frequency domain: Smoothing frequencydomain filtering, Sharpening frequency domain filtering.(Qualitatively)Segmentation & Morphological operations: segmentation and threshold function, Different algorithms in

thresholding, Line detection, Edge detection,Hough transform, Region based segmentation,

Morphological-dilation and erosion, opening and closing.(Qualitatively). Selection of sensors and

Processors for real time implementation.Shape Identification, Texture Identification, Colour Identification

Applications in Real Time Scenario using Rasperry Pi Processor.

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Course Outcomes:

CO1 To explain digital processing techniques like DFT,FFT and Digital Filters to various types ofsignals( Audio,video,electric signal, medical signals)

CO2 To review basic image sensing techniques , image representations and image transformsCO3 To employ various concepts of image enhancement techniques in spatial and frequency domain

to real time problemsCO3 To design suitable filter parameters forefficient image segmentation and other aspects of feature

analysis

CO4 To apply image processing techniques on an embedded platform ( Rasperry Pi)

TEXTBOOKS/REFERENCES

1. Mitra S. K, “Digital Signal Processing, A Computer-Based Approach”, Third Edition,McGraw Hill,2005.

2. Ifeachor E. C and Jervis B. W, “Digital Signal Processing: A Practical Approach”,SecondEdition, Addison Wesley, 2002.

3. Steven W Smith, “The Scientist and Engineer’s Guide to DSP”, Newnes, 2002.4. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Third Edition, Prentice Hall,

2008.5. AshwinPajankar, “Raspberry Pi Computer Vision Programming”, Packt Publishing, May 2015.6.

18ES603 EMBEDDED PROCESSOR ARCHITECTURES AND DESIGN3-0-2-4

An introduction to Embedded Processors – RISC verses CISC- CPU Performance MetricsBenchmark-Integer and Floating Point data representation - RISC processor designARM Architecture – Programming Model, Pipelined data path design - Pipeline Hazards, AddressingModes, ARM Instruction set - Thumb Instruction Set- ARM Programming - Vector Floating Point Unit,Interrupts & Exception Handling- DSP Extensions, Mixed C and Assembly programming, Memorysystem design- Cache Memory, Memory Management unit - Virtual Memory. Introduction to ARM basedMicrocontrollers – Peripherals – Interfacing – Application development – Case studies. ARM advancedCPU cores, Comparison with other architectures like PowerPC, DSP, PIC, MSP, FPGA, etc.

Course Outcomes:

CO1 Understand the architecture of ARM Processor

CO2 Analyse the instruction set and addressing modes of ARM Processor

CO3 Illustrate the interface of peripherals in ARM Microcontroller

CO4 Develop an embedded software for any given application

CO5 Evaluation of different micro controllers/DSP processors


1. Steve Furber, “ARM System-on-Chip Architecture”, Pearson India, 2015.

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2. Andrew Sloss, Dominic Symes and Chris Wright, “ARM System Developer's Guide: Designing andOptimizing System Software”, Morgan Kaufumann Publisher, 2011.

3. David A. Patterson and John L. Hennessy, “Computer Organization and Design – TheHardware/Software Interface”, ARM Edition, Morgan Kaufmann Publisher, 2010.

4. William Hohl and Christopher Hinds, “ARM Assembly Language: Fundamentals and Techniques”,Second Edition, CRC Press, 2016.

5. ARM Microcontroller User Manual.

18RM600 RESEARCH METHODOLOGY 2-0-0-2

Unit I:

Meaning of Research, Types of Research, Research Process, Problem definition, Objectives ofResearch,Research Questions, Research design, Approaches to Research, Quantitative vs. QualitativeApproach,Understanding Theory, Building and Validating Theoretical Models, Exploratory vs.ConfirmatoryResearch, Experimental vs Theoretical Research, Importance of reasoning in research.

Unit II:

Problem Formulation, Understanding Modeling & Simulation, Conducting Literature Review,Referencing,Information Sources, Information Retrieval, Role of libraries in Information Retrieval,Tools foridentifying literatures, Indexing and abstracting services, Citation indexes

Unit III:

Experimental Research: Cause effect relationship, Development of Hypothesis, MeasurementSystemsAnalysis, Error Propagation, Validity of experiments, Statistical Design of Experiments,FieldExperiments, Data/Variable Types & Classification, Data collection, Numerical and GraphicalDataAnalysis: Sampling, Observation, Surveys, Inferential Statistics, and Interpretation of Results

Unit IV:

Preparation of Dissertation and Research Papers, Tables and illustrations, Guidelines for writing theabstract,introduction, methodology, results and discussion, conclusion sections of a manuscript.References,Citation and listing system of documents

Unit V:

Intellectual property rights (IPR) - patents-copyrights-Trademarks-Industrial design geographicalindication. Ethics of Research- Scientific Misconduct- Forms of Scientific Misconduct.Plagiarism,Unscientific practices in thesis work, Ethics in science

Course Outcomes:

CO1 To introduce research through topic selection and report writing

CO2 Discuss research practices to read the literature and to identify the research gaps

CO3 Develop skills to present research ideas and prepare research report.

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CO4 To convey ethical practices in research with significance on citation

TEXT BOOKS/ REFERENCES:

1. Bordens, K. S. and Abbott, B. B., “Research Design and Methods – A Process Approach”,

8thEdition, McGraw-Hill, 2011

2. C. R. Kothari, “Research Methodology – Methods and Techniques”, 2nd Edition, New Age

International Publishers

3. Davis, M., Davis K., and Dunagan M., “Scientific Papers and Presentations”, 3rd Edition,

Elsevier Inc.

4. Michael P. Marder,“ Research Methods for Science”, Cambridge University Press, 2011

5. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008

6. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “Intellectual Property in New Technological

Age”. Aspen Law & Business; 6 edition July 2012

18ES621 DISTRIBUTED COMPUTING 3-0-0-3

Basics of real-time systems: Functional, Temporal and Dependability requirements, Introduction todistributed computing systems (DCS), DCS design goals, Transparencies, Fundamental issues, Systemarchitecture, Composability, Scalability, Extensibility, Complexity, Distributed and Centralizedarchitecture, Distributed Coordination:Temporal ordering of events, Lamport's logical clocks, Vectorclocks; Ordering of messages, Physical clocks, Global state detection, Process synchronization:Distributed mutual exclusion algorithms, Performance matrix.

Modeling distributed real-time systems, Real time communication, Requirements of real timecommunication system, Flow control-Explicit and Implicit, Thrashing, Inter-process communication:Message passing communication, Remote procedure call, Transaction communication, Groupcommunication; Broadcast atomic protocols, Deadlocks in distributed systems, Load scheduling andbalancing techniques, Consistency Models, Fault Tolerance.

Introduction to Distributed System Models, High-Performance Computing, Grid Computing, CloudComputing, Many-core Computing, Many-Task Computing, Data-Intensive Computing, Parallelarchitectures and Multithreaded programming.Introduction toGPU architecture and programming.Usage oftools for GPU Programming.

Course Outcomes:

CO1 Understand the basics of distributed computing systems

CO2 Describe various design goals of distributed computing systemsAnalyze the significance of time and various time synchronization methods in distributed

CO3 computing systems.

CO4 Examine the significance and requirements of real time communication systems.

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CO5 Illustrate various distributed system models


1. G. Coulouris, J. Dollimore, T. Kindberg, G. Blair, “Distributed Systems: Concepts and Design,”Addison Wesley, 2011.

2. Kai Hwang Jack Dongarra Geoffrey Fox, “Distributed and Cloud Computing” From ParallelProcessing to the Internet of Things, 1st Edition, Morgan Kaufmann 2011.

3. Andrew S. Tanenbaum and Maarten van Steen. “Distributed Systems: Principles and Paradigms”(DSPD), Prentice Hall, 2nd Edition, 2007

4. H Kopetz, “Real Time Systems: Design Principles for Distributed Embedded Applications”,Kluwer, 1997.

18ES622 INTERNET OF THINGS 3-0-2-4

Introduction to IoT - Definitions, frameworks and key technologies. Challenges to solve in IoT -Embedded systems architecture: Key hardware and software elements. Low power and very low powerembedded systems, peripherals and sensors in embedded systems, peripheral interfacing -SPI, I2C&CAN.Evolving IoT standards - Basics of Networking & Sensor Networks - Applications, challenges - ISO/OSIModel, TCP/IP Model, Network Devices and Applications.Communication Protocols - Wired andWireless LAN, Bluetooth, Zigbee, WiMax, etc. Communication models (Request-Response, Publish-Subscribe etc),IOT Protocols – MQTT, CoAP, HTTP REST, Web Sockets etc.System design of an IoT - Sensor network architecture, scenarios, optimization goals, design principles &Protocol Stack, Hardware and software protocol stacks modifications for IoT - MAC, Routing andapplication layers, performance considerations. Modern trends in IOT – Wearable, industrialstandards.Applications of IoT - Smart Homes/Buildings, Smart Cities, Smart Industry, and Smart Medicalcare, Smart Automation etc.

Course Outcomes:

CO1 Understand the underlying concepts and design principles of IoT

CO2 Understand various communication technologies

CO3 Analyse the performance of various IoT protocols

CO4 Choose an IoT protocol for a specific application

CO5 Design an IoT application


1. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, 5th Edition, PearsonEducation, 2011.

2. Steve Rackley, “Wireless Networking Technology - From Principles to Successful Implementation”,Newness (Elsevier), 2007.

3. Holger Karl and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, JohnWiley and Sons Ltd., 2005.

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4. Olivier Hersent, David Boswarthick and Omar Elloumi, “The Internet of Things: Key Applicationsand Protocols”, Wiley, 2012.

5. Vijay Madisetti and ArshdeepBahga, “Internet of Things: A Hands-on Approach”, Hardcover –Import, 2014.

18ES623 REAL TIME OPERATING SYSTEMS 3-0-2-4

Overview of concepts of GPOS, GPOS functionalities, Introduction to real-time systems,RTOS basicarchitecture, RTOS vs GPOS.Architecture of OS (Monolithic, Microkernel, Layered, Exo-kernel andHybrid kernel structures).Evolution of operating systems, POSIX Standards.RTOS Kernel, Kernel services: Task Management -tasks, process and threads, task attributes and types -task states and transition, task control block, Introduction to real-time task scheduling, uniprocessor andmultiprocessor scheduling concepts - RM, DM, EDF, Least Laxity First (LLF), clock-driven andpriority-driven scheduling, preemption-context switching, blocking, priority inversion and solutions,response time analysis, processor demand analysis,Worst case execution time calculation, scheduling forfault-tolerance.TimerManagement, Interrupt handling, MemoryManagement, Input-Output handling, TaskCommunication and Synchronization - Semaphores and Mutex, Mailbox, Queue, Pipes, RPC, deadlock.Comparison and study of RTOS: FreeRTOS and µCOS – Case studies: RTOS for fault TolerantApplications, picking RTOS for your project, RTOS trends in next 5 years.

Course Outcomes:

CO1 Understand the basic concepts in real time systems.

CO2 Illustrate various services provided by the RTOS Kernel

CO3 Develop various real-time scheduling algorithms for uni and multi processor systems.Analyse the schedulability of task sets using different tests and discuss blocking and priority

CO4 inversion in real time systems.

CO5 Design and develop real time applications using RTOS.


1. Jane W.S. Liu, “Real -Time Systems”, Pearson Education, 2000.2. Cheng, A. M. K., “Real-Time Systems: Scheduling, Analysis, and Verification”, Wiley, 2002.3. Krishna, C. M., Shin, K. G. “Real-Time Systems”, McGraw-Hill, 1997.4. Jean J Labrosse, “MicroC/OS-II-The Real-Time Kernel”, 2nd edition, CMP Books, 2002.5. Tanenbaum,”Modern Operating Systems,” 3/e, Pearson Edition, 2007.

18ES624 FPGA-BASED SYSTEM DESIGN 3-0-2-4

HDL – Role of HDL - HDL for Design Synthesis - Design Flow – Programmable logic: Simple PLDs,CPLDs ,FPGA HDL - A Simple Design – HDL elements - Data flow – behavioural – structural modeling -Creating Combinational and Synchronous Logic - Designing FIFO - Test Benches - State MachineDesigns - Design Examples - Memory Controller - Mealy State Machines - Design Considerations -Hierarchy in Large Designs - Functions and Procedures – Subprograms.

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General principles of circuit synthesis - Synthesis and Design Implementation - Synthesis and FittingCPLDs, FPGAs- Resource Sharing - Creating Test Benches – Implementation technology – PLD’s,Custom Chips, Standard Cell and Gate arrays – FPGA Architectures – SRAM based FPGAs –Permanently programmed FPGAs – Circuit design of FPGA fabrics – Architecture of FPGA fabrics –Logic Implementation of FPGAs - Physical design for FPGAs.

Course Outcomes:

CO1 Realization of combinational logic circuits in circuit level and using PLDs

CO2 Design combinational logic circuits using HDL

CO3 Design sequential logic circuits using HDL

CO4 Understand the design styles in different FPGA architectures

CO5 Synthesize digital circuits in FPGAs

TEXT BOOK / REFERENCES

1. Stephen Brown and ZvonkoVranesic, “Fundamental of Digital Logic with VHDL Design”, SecondEdition, McGraw Hill, 2000.

2. Douglas L Perry, “VHDL Programming by Example”, Fourth Edition, Tata McGraw Hill, 2002.3. Wayne Wolf, “FPGA-Based System Design”, Prentice Hall India Pvt. Ltd., 2004.4. Samir Palnitkar, “Verilog HDL,A Guide to Digital Design and Synthesis”, Second Edition, Pearson

Education, 2003.5. T. R. PadmanabhanandB. Bala Tripura Sundari, “Design Through Verilog HDL”, Wiley-Blackwell,

2003.

18ES625 EMBEDDED SYSTEM APPLICATION LAB 1-0-2-2

Each student in consultation with the faculty in-charge will select a topic related to embeddedsystems andapplications and develop an embedded product/system following the embedded system design principles.

Course Outcomes:

CO1 Analyze the requirement and feasibility of an application

CO2 Design the hardware/software architecture

CO3 Implement/develop the system

CO4 Test the system

18ES798 DISSERTATION 8

Each student should select and work on a topic related to his/her field of specializationduringsummer ofsecond semester under the supervision of a faculty member. By the end ofthe thirdsemester he/she mustprepare a report in the approved format and present it.

Course Outcomes:

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CO1 To select a topic and understand its global significance

CO2 To identify the research gaps and progress with implementation

CO3 To realise a fraction of the chosen topic in hardware/software

18ES799 DISSERTATION 12

During fourth semester each student should work further on the topic of the minor project oranew topicunder the supervision of a faculty member. By the end of fourth semester thestudenthas to prepare a reportin the approved format and present it.Finally there has to be a research paper published in a scopusindexed conference or journal with proper affiliation and approval from the department.

Course Outcomes:

CO1 To progress with the implementation

CO2 To improve comprehension with better analysis of work

CO3 To explore the work and publish with intermediate results

CO4 To implement the work using hardware/software and a thorough understanding

ELECTIVES

I. Embedded Applications

18ES701 EMBEDDED SYSTEMS FOR AUTOMOTIVE APPLICATIONS 3-0-0-3

Automotive Fundamentals – Vehicle functional domains and requirements – The systemsapproach tocontrol and automotive instrumentation – Sensors and actuators in various vehicledomains. Systems inPower train Electronics: Engine Management Systems: Spark Ignition,Petrol/ Diesel Injection Systems,Transmission Systems. Systems in Chassis control: ABS, ESP,TCS, Active Suspension Systems, Cruisecontrol and adaptive cruise control systems – Drive-bywiresystems. Body electronic systems: PowerGeneration/ Storage, starting motor systems,Vehicle wiring systems, HVAC, Automotive alarm systems,Vehicle immobilization &deactivation, Driver information systems, Parking systems, Central lockingsystem – electricwindows – Occupants and driver safety systems: Seat belt lighteners and air-bags –DiagnosticsSystems. Electric/Hybrid Vehicles and their configurations – Autonomous Vehicles and theirchallenges. Introduction to Embedded automotive protocols: LIN, CAN,FlexRay, MOST - AUTOSARstandard and its applications – OSEK/VDX Open Systems in AutomotiveNetworks.

Course Outcomes:

CO1 Understand thefundamentals of Automotive Sensors and Actuators

CO2 Describe the functionality of systems in Power Train and Body Electronics

CO3 Evaluate various Automotive Communication Protocols

CO4 Understand Automotive software standards

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1. William B. Ribbens, “Understanding Automotive Electronics - An Engineering Perspective”,Eight Edition, Elsevier Inc., 2017.

2. V. A. W. Hillier and David R. Rogers, “Hillier’s Fundamentals of Motor VehicleTechnologyon Chassis and Body Electronics”, Fifth Edition, Nelson Thrones, 2007.

3. Robert Bosch GmbH, “Bosch Automotive Electricsand Automotive Electronics - Systems andComponents,Networking and Hybrid Drive”, Fifth Edition, Springer Vieweg, 2007.

4. Joseph Lemieux, “Programming in the OSEK/VDX Environment”, CMP Books, USA, 2001.5. Tom Denton, “Automobile Electrical and Electronic Systems”, Third Edition, Elsevier Butterworth-

Heinemann, 2004.

18ES702 ADVANCED MOBILE AND WIRELESS NETWORKS 3-0-0-3

Overview of Wireless Systems, TeleTraffic Engineering-Service level, Usage, MeasurementUnits, Types,B Formulas, Overview of Digital Communication and Transmission, MultipleAccess Techniques,Architecture of Wireless Wide-Area Network, Mobility Management,Mobile Network and TransportLayer- TCP/IP Suite for Wireless Networks, Mobile IP, SIP,Wide Area Wireless Network Service-GSM,3G, UMTS, QoSMangament, HSDPA, FOMA,CDMA, Wireless Application Protocol, BluetoothProtocol stack, Link Types, Security, ErrorCorrection, Topology, Applications, WiMax, 4G Systems,Software Defined Radio, CognitiveRadio.

Course Outcomes:

CO1 Understand the different wireless communication systems and multiple access techniques

CO2 Distinguish various architecture of wireless WAN

CO3 Discuss wireless service networks like GSM CDMA UMTS, HSDPA and 3G.

CO4 Design an wireless application for a real world problem


1. Vijay K Garg, “Wireless Communications and Networking”, Morgan Kaufmann, 2007.2. Adreas F Molisch, “Wireless Communications”, Second Edition, Wiley, 2011.3. William Lee, “Wireless and Cellular Telecommunications”, Third Edition, McGraw Hill,2005.4. Martin Sauter, “Beyond 3G - Bringing Networks, Terminals and the Web Together:

LTE,WiMAX, IMS, 4G Devices and the Mobile Web 2.0”, Wiley, 2009.5. EldadPerahia, “Next Generation Wireless LANs: Throughput, Robustness, and

Reliability in802.11n”, Cambridge University Press, 2008.

18ES703 EMBEDDED SYSTEMS IN BIOMEDICAL APPLICATIONS 3-0-0-3

2 0

Overview of biomedical devices – Origin of bio potentials – bio potential electrodes – biopotentialamplifiers, System Theory for Physiological Signals: Filters, Modeling – Embeddedsystems in Patientmonitoring: ECG, EEG, EMG, Blood pressure, respiration, pulse oxymeters, diagnostic devices.Noninvasive Diagnosis Using Sounds from Within the Body, Noninvasive Measurement of BloodPressure, Measurement of Electrical Potentials and Magnetic Fields from the Body Surface andPlethysmography.Healthcare and the Wireless Sensor Network, Smart m-Health Sensing, m-Health andMobile Communication Systems, Data Collection and Decision Making.m-Health Computing m-Health2.0,Social Networks, Health Apps, Cloud and Big Health Data, m-Healthand Global Healthcare and the Future of m-Health – case study.

Course Outcomes:

CO1 Understand the basics of Bio Potentials and Physiological Signals

CO2 Overview on Patient Monitoring using Embedded Systems

CO3 Review on Embedded Systems in Patient Assistive DevicesAnalyse the application of Embedded systems in surgical devices, medical imaging, clinical

CO4 laboratory equipment and so on


1. John G. webster, “Medical Instrumentation - Application and Design”, Fourth Edition, JohnWileyand Sons, 2010.

2. Subhas Chandra Mukhopadhyay and Aime Lay-Ekuakille, “Advances in BiomedicalSensing, Measurements, Instrumentation and Systems”, Springer, 2010.

3. Aime Lay-Ekuakille and Subhas Chandra Mukhopadhyay, “Wearable andAutonomousBiomedical Devices and Systems for Smart Environment - Issues andCharacterization”, Springer, 2010.

4. Robert B. Northrop, “Noninvasive Instrumentation and Measurement in Medical Diagnosis”, CRCPress, 2002.

5. Roberts. H. Istepanian and Bryan Woodward, “m-Health Fundamentals and Applications”, Wiley,2017.

18ES704 EMBEDDED SYSTEMS IN ROBOTICS 3-0-0-3

Robots and Embedded Systems-Sensors - Sensor Categories, Binary Sensor, Analog versus DigitalSensors, Shaft Encoder; A/D Converter, Position Sensitive Device; Compass, Gyroscope, Accelerometer,Inclinometer, Digital Camera. Actuators - DC Motors, H-Bridge, Pulse Width Modulation, StepperMotors, Servos. Control - On-Off Control, PID Control, Velocity Control and Position Control, EmbeddedControllers, Interfaces, Operating System. Industrial Robots - Evolution of robotics, Robot anatomy,Design and control issues, Manipulation and Control. Direct Kinematic Model - Denavit-HartenbergNotation, Kinematic Relationship between adjacent links, Manipulator Transformation Matrix; InverseKinematic Model – Manipulator Workspace, Solvability, Solution techniques, Closed form solution.Mobile Robots, Concepts of Localization and path planning. Autonomous robots and Introduction toRobot Operating System.

Course Outcomes:

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CO1 Understand the principle of operation of sensors and actuators used in robotics.Understand the interfacing of I/O devices, communication modules and embedded controllers for

CO2 robotics application.

CO3 Develop the kinematic models of manipulators.

CO4 Review of algorithms in autonomous mobile robot path planning, localization and control.

CO5 Design of algorithms for various robotics applications.


1. Thomas Bräunl, “Embedded Robotics: Mobile Robot Design and Applications withEmbedded Systems”, Third Edition, Springer-Verlag Berlin Heidelberg, 2008.

2. R.K.Mittal and I.J.Nagrath, “Robotics and Control”, Tata McGraw-Hill Publishing Company Ltd.,New Delhi, 2003.

3. John J. Craig, “Introduction to Robotics: Mechanics and Control”, Third Edition, Pearson/PrenticeHall, 2005.

4. AnisKoubaa, "Robot Operating System (ROS) The Complete Reference”, First Volume, Springer,2016.

5. K.S. Fu, R.C. Gonzalez and C.S.G. Lee, “Robotics: Control, Sensing, Vision,and Intelligence”, McGraw-Hill, New York, 1987.

18ES705 EMBEDDED SYSTEMS IN SMART GRID 2-0-2-3

Smartgriddefinition.Smartgridvs conventional grid.SmartGrid technologies- Power system and ICT inGeneration,Transmission and Distribution. Basic understanding of power systems.Managment aspects(Utility, Operator, Consumer).Evolution of automation in power system.Smart Grid features- Distributed generation,storage,DD,DR,AMI,WAMS,WACS). Sensors - CT, PT;Embedded Devices - IED, PMU, PDC, CT, PT, relays, DR Switch; Algorithms; Communication-Standards, Technology and protocols. IoT applications in power system – Case study 1 generation control,load management, dynamic pricing etc; IoT for domestic prosumers. Case Study 2 -Smart microgridsimulator (SMGS),DR,DD,Energystorage,Communication.

Course Outcomes:

CO1 Understanding the basics of power system management and its automation

CO2 Explore the features of Smart grid

CO3 Learn different Sensors and embedded devices used in smart grid

CO4 Examine the different communication standards, technologies and protocols for smart grid

CO5 Investigate the IoT applications in Smart grid


1. James Momoh, “Smart Grid: Fundamentals of Design and Analysis”, Wiley‐IEEE Press, March 2012.

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2. JanakaEkanayake, Nick Jenkins, KithsiriLiyanage, Jianzhong Wu and Akihiko Yokoyama, “SmartGrid:Technology and Applications”, Wiley, February 2012.

3. NouredineHadjsaïd and Jean-Claude Sabonnadière, “Smart Grids”, Wiley-ISTE, May 2012.4. Ali Keyhani and Muhammad Marwali, “Smart Power Grids 2011”, Springer, 2011.5. Mini S. Thomas, John Douglas McDonald,"Power System SCADA and Smart Grids", CRC Press,

April 2015.6. Vijay Madisetti and ArshdeepBahga, “Internet of Things: A Hands-on Approach”,Hardcover – Import,

2014.

18ES706 DESIGN FOR INTERNET OF THINGSAND CLOUD COMPUTING 2-0-2-3

Embedded Systems: Rise of embedded systems and their transition to intelligent systems and to Internetof Things -RFIDs, NFC, Web of Things - Embedded Systems Design: Partitioning to hardware andsoftware; principles of co-design; performance of these systems - estimation of speed, throughput, powerand energy consumption; hardware design elements -design, validation, and testing tools; softwareplatforms –OS and applications, code optimization, validation and robust code generation; systemintegration, debugging and test methodology; tools for coding, debugging, optimization, anddocumentation; measurement of system performance, Creating virtual prototypes -hardware softwareemulation. IOT Reference Architectures, Introduction to Node Red, Visual Prototyping with Arduino andconnectivity to IOT platforms, Applications: Healthcare and home automation examples.Cloud Computing: Infrastructure as a Service (IaaS), Cloud Database, Cloud Storage.Platform as aService (PaaS) for Web Rapid Application Development (RAD), Distributed Storage, DistributedComputing frameworks.Connectivity to remote server database, data access-storage-processing.Development of cloud server and web applications.

Course Outcomes:

CO1 Understand the challenges and requirement of IoT framework.

CO2 Distinguish applications from ubiquitous computing, IoT and WoT.Discuss the issues in system integration, debugging, testing and analysing the system

CO3 performance.

CO4 Design an IoT application

TEXTBOOKS / REFERENCES:

1. Barry, P., and Crowley, P., Modern Embedded Computing, Morgan Kaufmann, 2012.2. Wolf, M., Computers as Components, Third Edition, Morgan Kaufmann, 2012.3. VijayMadisetti and ArshdeepBahga, “Internet of Things: A Hands-on Approach”, Hardcover – Import,

2014.4. Thomas Erl, "Cloud Computing: Concepts, Technology & Architecture", Prentice Hall, May 2013.5. Michael J. Kavis, "Architecting the Cloud: Design Decisions for Cloud Computing Service Models

(SaaS, PaaS, &IaaS)", Wiley CIO Series, January 2014.6. George Reese, "Cloud Application Architectures: Building Applications and Infrastructure in the

Cloud", O'Reilly, 2009.

18ES707 SPECIAL TOPICS IN EMBEDDED SYSTEMS 3-0-0-3

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System analysis-user and market requirement, project specifications, structured analysis. SystemModelling, Preliminary design – Hardware and Software Partitioning, Component Selection - Selectingthe right sensors, processors, interfacing circuits, actuators, software modules and developmentenvironments, Testing and Debugging, User interface design and prototyping.Approaches toimplementation-top down, bottom up, threads.

Major trends in embedded system - Processor - Single core, Multi-core, Multi-processor, Pervasive orubiquitous or context-aware computing, Artificial Intelligence, Virtual and Augmented Reality, Embeddedsoftware, IoT, Computer/Machine vision, Cyber-physical systems.

Applications and Use cases - Wearable Electronics, Smart Healthcare, Automotive Embedded Systems,Smart agriculture, Consumer electronics like PDA, mobile phones, Smart homes/environments, SmartCommunity, Smart citiesandSmart grids, Virtual assistants, Robotics, Smart Security/Surveillancesystems,Autonomous systems.

Course Outcomes:

CO1 Understand the process of embedded system design

CO2 Compare various hardware and software modules

CO3 Select suitable architecture for system design

CO4 Design an embedded system for real world application


1. Wayne Wolf, “Computers as Components, Second Edition: Principles of Embedded ComputingSystem Design”, 2nd Edition, The Morgan Kaufmann Publishers, 2008.

2. E. A. Lee and S. A. Seshia, Introduction to Embedded Systems - A Cyber-Physical SystemsApproach, Second Edition, MIT Press, 2017.

3. A. K. Ganguly, “Embedded Systems: Design, Programming and Applications”, Narosa, 2014.4. Raj Kamal, “Embedded Systems - Architecture, Programming and Design”, Tata-McGrawHill, 12th

Reprint, 2007.5. Raul A. Santos, Arthur Edwards Block, “Embedded Systems and Wireless Technology: Theory and

Practical Applications”, CRC Press, 2012.

II. Architecture and Programming

18ES708 MULTI-CORE ARCHITECTURES 3-0-0-3

Review of Computer Design - Basics of Pipelining - Hazards, Measuring performance Instruction levelparallelism - Branch prediction techniques - Static & Dynamic scheduling – Speculation - Limits of ILP.Thread-level parallelism, Multi-issue and Multi-core processors -Shared and Distributed memoryMultiprocessor Architectures - Transaction Memory issues Memory hierarchy design - Cache coherence,Memory wall problem - Advanced Cache Memorydesign - Virtual Memory, Storage Systems - WarehouseScale Computers - Power optimizationin multi-core systems - Multi-core architectures for embeddedsystems – Programmingenvironments for multi-core.

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Course Outcomes:

CO1 Understand the basics of pipeline concepts and hazards

CO2 Understand instruction level and thread level parallelism and branch prediction techniques

CO3 Develop static and dynamic scheduling algorithmsDiscuss multiprocessor architectures and concepts on multi-issue and multi-core processors with

CO4 power optimization

CO5 Analyze memory hierarchy design and cache coherency problem


1. J.L. Hennessy and D.A. Patterson, “Computer Architecture: A QuantitativeApproach”, FifthEdition, Morgan Kaufmann, 2011.

2. GeorgiosKornaros, “Multi-core Embedded Systems”, CRC Press, Taylor and Francis Group,2010.3. J.P. Shen and M.H. Lipasti, “Modern Processor Design: Fundamentals of SuperScalarProcessors”,

McGraw Hill, 2005.4. David Culler, J.P. Singh and Anoop Gupta, “Parallel Computer Architecture:

AHardware/Software Approach”, Morgan Kaufmann, 1998.5. DezsoSima, Terence Fountain and Peter Kacsuk, “Advanced Computer

Architectures: A Design Space Approach ”,Pearson, 2005.

18ES709 FAULT TOLERANT SYSTEMS 3-0-0-3

Hardware fault tolerance, software fault tolerance, information redundancy, check pointing, fault tolerantnetworks, reconfiguration-based fault tolerance, and simulation techniques. Students will gain familiaritywith the core and contemporary literature in the area for dependable computing. Dependability concepts:Dependable system, techniques for achieving dependability, dependability measure, fault, error, failure,and classification of faults and failures. Fault Tolerance Strategies: Fault detection, masking, containment,location, reconfiguration, and recovery. Fault Tolerant Design Techniques: Hardware redundancy,software redundancy, time redundancy and information redundancy. Dependable communication:Dependablechannels, survivable networks, fault-tolerantrouting. Fault recovery, Stable storage and RAIDarchitectures, and Data replication and resiliency. Case studies of fault tolerant multiprocessor anddistributed systems.

Course Outcomes:

CO1 Understand basics of fault tolerance and discuss various forms of redundancies to mask thefailures in a system.

CO2 Discuss check pointing, process resilience and recovery strategies in distributed systems.

CO3 Analyze fault tolerant networks and reconfiguration-based fault tolerance schemes.

CO4 Develop concepts on dependability and design of fault tolerant distributed systems.

CO5 Comprehend reliable communication and factors affecting communication latency.


1. Israel Koren and C. Mani Krishna, “Fault Tolerant Systems”, Elsevier.2007.

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2. P. Jalote, “Fault Tolerance in Distributed Systems”, Prentice-Hall Inc. 1994.3. D. K. Pradhan, “Fault-Tolerant Computing, Theory and Techniques”, Prentice-Hall, 1998.4. Los Alamitos, CA, “Fault-Tolerant Computing, Theory and Techniques”, IEEE ComputerSociety

Press, 1996.5. Barry W. Johnson, “Design and Analysis of Fault-Tolerant Digital System”, Addison, 1989.

18ES710 GPU ARCHITECUTRE AND PROGRAMMING 2-0-2-3

Introduction to Parallel Programming - Introduction to OpenCL - OpenCL Device Architectures - BasicOpenCL – examples - Understanding OpenCL - Concurrency and Execution Model - Dissecting aCPU/GPU - OpenCL Implementation - OpenCL case study: Convolution, Video Processing, Histogramand Mixed Particle Simulation - OpenCL Extensions - OpenCL Profiling and Debugging – WebCL.

Course Outcomes:

CO1 Understand the fundamentals of prallel programming

CO2 Discuss various OpenCL device architectures

CO3 Anayse an OpenCL case study

CO4 Develop an application using GPU


1. Benedict R Gaster, Lee Howes, David, R. Kaeli, PerhaadMistry and Dana Schaa, “HeterogeneousComputing with OpenCL”, Second Edition, Elsevier, 2012.

2. AaftabMunshi, Benedict Gaster, Timothy G. Mattson, James Fung and Dan Ginsburg, “OpenCLProgramming Guide”, Addison-Wesley Professional, 2011.

3. RyojiTsuchiyama, Takashi Nakamura, TakuroIizuka and Akihiro Asahara, “TheOpenCL Programming Book ”, Fixstars Corporation, 2010.

4. Matthew Scarpio, “OpenCL in Action: How to Accelerate Graphics andComputations”, ManningPublications, 2011.

18ES711 SOFT COMPUTING 3-0-0-3

Neural Networks (NN) – Supervised and Unsupervised Learning – Hopfield – RBF Networks – PrincipalComponent Analysis – PNN – Kohonen Self Organizing Networks – Learning Vector Quantization –Hebbian Learning – Adaptive Resonance Theory – Genetic Algorithms (GA) – Standard GA – SchemaTheory – Building Block Hypothesis – Introduction to Support Vector Machines – Classification andRegression – Typical Applications Integrating Various Soft Computing Tools. Introduction toevolutionary algorithms-Ant Colony Optimization and swarm intelligence.

Course Outcomes:

CO1 Generate Neural Network models from supervised and unsupervised algorithms

CO2 Model Genetic algorithm based search applications

CO3 Build support vector classification and regression models for basic applications

CO4 Understand the different evolutionary computational algorithms

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1. Simon Haykin, “Neural Networks and Learning Machines”, Third Edition, Pearson Education, 2009.2. K.F. Man, K.S. Tang and S. Kwong, “Genetic Algorithms: Concepts and

Applications”, IEEE Transactions on Industrial Electronics, Vol-3, 1996.3. Thomas S.Parker and Leon O Chua, “CHAOS: A Tutorial for Engineers”, IEEE Proceedings, Vol-75,

No.8, 1987.4. Jan Komorowski, Lech Polkowski and AndrzejSkowron, “Rough Sets: A Tutorial”,

http://Folli.Loria.Fr/Cds/1999/Library/Pdf/Skowron.Pdf

18ES712 HARDWARE SOFTWARE CO-DESIGN 3-0-0-3

Introduction to system level design, Models of computation for Embedded Systems: Models taxonomy,State-Oriented & Activity Oriented Models, Structure & Data –Oriented Models, Heterogeneous Models,Architectural selection: Architecture taxonomy, Architectural Models, RISC,CISC, SIMD,MIMD,Partitioning:Types, issues, Hardware Software Partitioning Algorithms, Scheduling and communication:Estimations, Scheduling, allocation and binding. Scheduling Algorithms, Simulation, synthesis andverification: High Level Synthesis, Logic Synthesis and verification, Implementation case studies,Performance Analysis and Optimization, Retargetable code generation, FPGAs.

Course Outcomes:

CO1 Introduce the various models of computation for embedded systemsUnderstand the architectural selection, partitioning, scheduling and communication for embedded

CO2 system applicationsApply the simulation, synthesis and verification for FPGA implementation of embedded system

CO3 case studies

CO4 Analyze the performance and optimization of FPGA implementations

CO5 Realize the retargetable code generation through hardware implementations


1. D Gajski, F Valhid, S Narayan and J Gong, “Specification and Design of EmbeddedSystems”, Prentice Hall PTR, 1994.

2. Jorgen Staunstrup and Wayne Wolf, “Hardware / Software Co-Design: Principle andPractice”, Kluwer Academic, 1997.

3. Ti - Yen Yen and Wayne Wolf, “Hardware-Software Co-Synthesis of DistributedEmbedded Systems”, Kluwer, Reprint 2010.

4. Peter Marwedel, “Embedded System Design”, Kluwer Academic Publishers, 2003.5. Joris van den Hurk and Jochen A.G. Jess, “System Level Hardware/Software CoDesign: AnIndustrial

Approach”, Springer, 1997.

18ES713 OBJECT ORIENTED PROGRAMMING 3-0-0-3

Introduction to object oriented software design, Comparison of programming methodologies, ObjectBasics, Java Environment, Classes and Object, Data Members, Access Specifiers, Arrays within a Class,

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http://folli.loria.fr/Cds/1999/Library/Pdf/Skowron.Pdf

Array of Objects, Constructors, Default Constructors, Destructors, Static Members, Constant Members,Object Oriented Design with UML, Class s , object diagrams and sequence diagrams. Overview of Streams,Bytes vs. Characters, File Object, Binary Input and Output, Reading and Writing Objects, MethodOverriding, Polymorphism, Super, Interfaces and Abstract Classes, Packages, Use case diagrams andactivity diagrams. Introduction to Threads, Creating Threads, Thread States, Runnable Threads,Coordinating Threads, Interrupting Threads, Runnable Interface Applets: Applet Architecture- Parametersto Applet - Embedding Applets in Web page, Component diagrams and Deployment diagrams.

Course Outcomes:

CO1 Understand the basics of OOPs concepts

CO2 Apply the oops concept for typical programs

CO3 Design of UML for typical applications

CO4 Develop software using OOPs

TEXT BOOK / REFERENCES:

1. Naughton P. and Schildt H., “Java2 Complete Reference”, Eighth Edition, Tata GrawHill, 2011.2. Ali Bahrami, “Object Oriented Systems Development”, Second Edition, McGraw-Hill, 2008.3. Grady Booch and Robert A. Maksimchuk, “Object-oriented Analysis and Design

with Applications”, Third Edition, Addison Wesley, 2006.4. Jaime Nino, Fredrick a Hosch, “An Introduction to Programming and Object Oriented

Design Using Java”, Wiley India PrivateLimited, 2010.

18ES714 MACHINE LEARNING 3-0-0-3

Introduction to Machine learning, different forms of learning: supervised and unsupervised learning,classification and regression, parametric and nonparametric models, curse of dimensionality, linear andlogistic regression, Basics of probability theory and probability distributions, information theory, Bayesianlearning, Neural Networks, Gaussian Mixture models and the EM algorithm, Factor analysis, Principalcomponents analysis, Independent Component Analysis, Kernels and kernel functions, Support vectormachines for regression and classification, Decision trees, CART, Ensemble Methods: Boosting -Adaboost, Gradient Boosting; Bagging - Simple methods, Random Forest, Markov and hidden Markovmodels, Introduction to deep learning, Examples and case studies in machine learning.

Course Outcomes:

CO1 Generate basic machine learning models from supervised and unsupervised algorithms

CO2 Construct learning algorithms using Bayesian probabilistic and Gaussian Mixture models Performdimensionality reduction using Principal components analysis, Independent Component

CO3 Analys i sBuild training and prediction algorithms for classification using decision trees, artificial neural

CO4 networks and Support Vector Machines


1. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.

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2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012.3. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical

Learning: Data Mining, Inference, and Prediction”, Second Edition, Springer, 2009.4. Bernhard Schölkopf and Alexander J. Smola, “Learning with Kernels - Support

Vector Machines, Regularization, Optimization, and Beyond”, MIT Press, 2001 .5. Tom M. Mitchell, “Machine Learning”, McGraw-Hill, 1997.

18ES715 DEEP LEARNING 2-0-2-3

Probability and Information Theory , Basics of classical Machine Learning techniques, algorithmicdifferentiation-forward and backward. Introduction to Neural Networks, Backpropagation, Multi-layerPerceptrons . Overview of Computer vision Problems, Deep Learning for computer vision, DeepFeedforward Networks , Regularization for Deep Learning Optimization for Training Deep Models ,Convolutional Networks , object detection and segmentation, Visualization and understanding, Generativemodels, Variationalautoencoders, Sequence Modeling: Recurrent and Recursive Nets. Long short termmemory networks (LSTM ) . Applications in security and Autonomous navigation.

Course Outcomes:

CO1 Generate learning models from perceptron and backpropagation algorithms

CO2 Understanding the basics of deep learning

CO3 Realizing applications using Convolutional Networks

CO4 Train neural models using autoencoders and LSTM networks

CO5 Perform sequence modeling using Recurrent networks


1. Ian Goodfellow, YoshuaBengio and Aaron Courville, “Deep Learning”, MIT Press, 2017

III. Controls and Systems

18ES716 CRYPTOGRAPHY AND NETWORK SECURITY 3-0-0-3

Classical Encryption Techniques – Symmetric Cipher Model – Steganography – AESCipherSymmetricCipher – Multiple Encryption and triple DES – Blocks Cipher – stream Cipher – Confidentiality usingsymmetric encryption – Placement of encryption function – randomnumber generation – Introduction tonumber theory – Cryptosystems – message authenticationand Hash functions – requirements – functions –course – Hash and MAC algorithms – secureHash algorithms – Digital signatures and authenticationprotocols – standard – authenticationapplications – Electronic mail security - S/MIME-IP security –overview- architecture – websecurity - socket layer and transport layer security – Intruders – Detection –Malicious software– viruses and related threats – counter measures – firewalls – design principles – trustedsystems.

Course Outcomes:

CO1 Understand various encryption techniques

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CO2 Understand the requirements of number theory in cryptographic schemes

CO3 Illustrate various authentication protocols

CO4 Analyse various software threats and counter measures


1. William Stallings, “Cryptography and Network Security – Principles and Practices”, Fourth Edition,Prentice Hall, 2003.

2. Douglas R Stinson, “Cryptography: Theory and Practice”, Third Edition, Chapman and Hall/CRC,2005.

18ES717 SPEECH AND LANGUAGE PROCESSING 3-0-0-3

Introduction to Linguistics – natural language and formal language – regular expressions and finite stateautomata – words and their parts – morphology – parsing – word tokenization – pronunciation and spelling– N grams and language models – Transliteration – Transliteration using sequence labeling – Part ofspeech tagging – POS tagging using SVM – chunking – shallow parsing – context free grammars –Parsing using context free grammars – probabilistic and lexicalized parsing, CFG parser – Parsingtechniques – structured output learning – generalized linear classifiers in NLP.

Course Outcomes:

CO1 To familiarize natural language and formal language using regular expressions and finite stateautomata

CO2 To understand language models and transliteration using sequence labeling

CO3 To perform speech tagging and parsing using machine learning technique like SVM

CO4 To introduce linear classifiers in natural language processing


1. Daniel Jurafsky and James H Martin, “Speech and Language Processing: An Introduction to NaturalLanguage Processing, Computational Linguistics and Speech Recognition”, Second Edition, PrenticeHall, 2008.

2. Christopher D. Manning and HinrichSchutze, “Foundations of Statistical Natural LanguageProcessing”, MIT Press, 1999.

3. Sandra Kubler, Ryan McDonald and JoakimNivre, “Dependency Parsing Synthesis Lecturers onHuman Language Technologies”, Morgan and Claypool Publishers, 2009.

18ES718 ADVANCED DIGITAL SIGNAL PROCESSING AND PROCESSORS 3-0-0-3

Multirate Digital Signal Processing - Decimation, Interpolation, Cascade Equivalents, Fractional SamplingRate Alteration, Applications- acquisition of high quality data, high resolution spectral analysis and designand analysis of narrowband digital filtering. Architecture of TMS320C6748 DSP - Instruction set –Addressing modes - Peripherals - Assembly language and C programming – Integrated DevelopmentEnvironment - Code Composer Studio.Digital Signal Processing implementation using TMS320C6748

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DSP for digital filters - Levinson Durbin Algorithm Embedded Target for TMS320C6748 DSP Platformusing MATLAB - Simulink.Application development using MATLAB – Simulink - Real-TimeWorkshopand hardware.

Course Outcomes:

CO1 To understand multirate signal processing concepts by analyzing simple problems

CO2 To familiarize with the architecture and addressing modes of TMS320C67xx DSP

CO3 To develop software solutions for TMS320 C67xx using assembly and high level languages

CO4 To investigate integration of MATLAB-Simulink with DSP platform


1. Vaidyanathan P. P, “Multirate Systems and Filter Banks”, Prentice Hall, 1993.2. TMS320C6748 DSP. Technical Reference Manual.3. Andy Bateman and Iain Paterson-Stephens, “The DSP Handbook, Algorithms,Applications and

Design Techniques”, Prentice-Hall, 2002.4. RulphChassaing, “DSP Applications Using C and the TMS320C6x DSK”, John Wiley and Sons,

2002.5. B Venkataramani and M Bhaskar, “Digital Signal Processors: Architecture,Programming and

Applications”, Tata McGraw Hill, 2002.

18ES719 MODERN CONTROL SYSTEMS 3-0-0-3

State Variable Analysis and Design: Introduction, concept of state, state variables and state model, statemodeling of linear systems, linearization of state equations. State Space representation using physicalvariables, Phase variables and Canonical variables. Derivation of transfer function from state model,diagonalization, Eigen values, Eigen vectors, generalized Eigen vectors. Solution of state equation, statetransition matrix and its properties, computation using Laplace transformation, power series method,CayleyHamilton method, concept of controllability and observability, methods of determining the same.Pole Placement Techniques: Stability improvements by state feedback, necessary and sufficient conditionsfor arbitrary pole placement, state regulator design, and design of state observer, Controllers-P, PI, PID.Non-linear systems: Introduction, behavior of non-linear system, common physical non linearity-saturation, friction, backlash, dead zone, relay, multivariable non-linearity. Phase plane method, singularpoints, stability of nonlinear system, limit cycles, construction of phase trajectories. Liapunov stabilitycriteria, Liapunov functions, direct method of Liapunov and the linear system, Hurwitz criterion andLiapunov’s direct method, construction of Liapunov functions for nonlinear system.

Course Outcomes:

CO1 Review of linear system in state space approach

CO2 Determine the solution to state equation

CO3 Design state feedback controller and observer for linear systems

CO4 Analyse non-linear system characteristics and stability criteria

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TEXT BOOKS/ REFERENCES:

1. Ogata, “Modern Control Engineering”. Fifth Edition, Prentice Hall, 2009.2. Franklin and Powell, “Feedback Control of Dynamics Systems”. Fourth Edition, PrenticeHall, 2002.3. Joseph DiStefano III, Allen J. Stubberud and Ivan J. Williams “Feedback and Control

Systems”,Second Edition, Schaum’s Outline Series, Mcgraw-Hill, 2014.4. David G. Luenberger, “Introduction to Dynamic Systems: Theory, Models, and

Applications”, Wiley, 1979.5. Richard C. Dorf and Robert H. Bishop, “Modern Control Systems”, Eleventh Edition Prentice Hall,

Pears Education, 2008.

18ES720 VIDEO PROCESSING 3-0-0-3

Introduction - Image Representation - Image Digitization, Geometric Transformations, Linear imagefiltering and correlation- Image Smoothing - Edge Detectors - Corner Detectors. Noise reduction, ImageSegmentation, Morphological image processing, Digital video processing.

Course Outcomes:

CO1 To explain digital processing techniques - DFT,FFT and Digital Filters applied to continuoustime signals

CO2 To apply image smoothing and linear filters to video frames.

CO3 To perform various image segmentation techniques on video frames

CO4 To perform morphological operations on digital videos.


1. Daniel Jurafsky and James H Martin, “Speech and Language Processing: An Introduction to NaturalLanguage Processing, Computational Linguistics and Speech Recognition”, Second Edition, PrenticeHall, 2008.

2. Christopher D. Manning and HinrichSchutze, “Foundations of Statistical Natural LanguageProcessing”, MIT Press, 1999.

3. Sandra Kubler, Ryan McDonald and JoakimNivre, “Dependency Parsing Synthesis Lecturers onHuman Language Technologies”, Morgan and Claypool Publishers, 2009.

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PROGAM M.TECH. in Embedded Systems · 18ES701 Embedded Systems for Automotive Applications 3 0 0 3...

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